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MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It excels at handling unstructured or semi-structured data, hierarchical relationships, and scenarios requiring horizontal scaling.
Key Features:
Flexible schema (schemaless documents)
Rich query language with secondary indexes
Aggregation framework for analytics
Horizontal scaling (sharding)
Replica sets for high availability
Change streams for real-time data
Geospatial and full-text search
When to Use MongoDB:
Rapidly evolving schemas
Hierarchical/nested data (embedded documents)
Real-time analytics with aggregation
Geospatial applications
Content management systems
IoT data ingestion
Catalog/inventory systems
When NOT to Use MongoDB:
Complex multi-table joins (use RDBMS)
ACID transactions across many documents (improved in 4.0+, but limited)
// Spring Boot configuration
spring:
data:
mongodb:
uri: mongodb://localhost:27017/mydb
auto-index-creation: false # Create indexes manually in production
# Connection pool settings(via URI)
# mongodb://localhost:27017/mydb?maxPoolSize=50&minPoolSize=10&maxIdleTimeMS=30000
Query Optimization
// Use projections to limit returned fields
db.users.find({ status: "active" }, { name: 1, email: 1 })
// Use hint to force specific index
db.users.find({ status: "active" }).hint({ status: 1, createdAt: -1 })
// Limit results for pagination
db.users.find().sort({ createdAt: -1 }).skip(20).limit(10)
// Use $exists: false for missing fields (can use index)
db.users.createIndex({ optionalField: 1 }, { sparse: true })
db.users.find({ optionalField: { $exists: true } })
Schema Optimization
// Avoid large arrays (cap at reasonable size)// Use bucketing pattern for time-series data
{
_id: "sensor1_2024-01-15",
sensorId: "sensor1",
date: ISODate("2024-01-15"),
readings: [
{ ts: ISODate("..."), value: 23.5 },
{ ts: ISODate("..."), value: 24.1 },
// ... up to N readings per bucket
],
count: 288// Track count for full bucket detection
}
// Pre-aggregate for reporting
{
_id: "stats_2024-01",
month: "2024-01",
totalOrders: 1523,
totalRevenue: 152300.50,
avgOrderValue: 100.00,
topProducts: ["SKU001", "SKU002", "SKU003"]
}
Best Practices
1. Schema Design
// Embed when: data is queried together, bounded arrays// Reference when: unbounded arrays, many-to-many, independent access// Use extended reference pattern for frequently accessed fields
2. Indexing
// Create indexes for query patterns, not just fields// Use compound indexes following ESR rule// Monitor slow queries: db.setProfilingLevel(1, { slowms: 100 })// Avoid indexing low-cardinality fields alone
3. Write Operations
// Use bulk operations for multiple writes// Avoid unbounded array growth// Use write concern appropriate to durability needs
db.orders.insertOne(doc, { writeConcern: { w: "majority" } })
4. Read Operations
// Always use projections to limit returned data// Use explain() to verify index usage// Prefer aggregation over multiple queries// Use read preference for scaling reads
db.orders.find().readPref("secondaryPreferred")
5. Connection Management
// Use connection pooling// Set appropriate pool size (default: 100)// Handle connection errors with retry logic// Close connections properly on shutdown
Common Pitfalls
Unbounded array growth:
// BAD: Comments array grows forever
{ _id: "post1", comments: [...thousands of comments...] }
// GOOD: Separate collection with references
{ _id: "comment1", postId: "post1", text: "..." }
Missing indexes:
// Always create indexes for query patterns// Check with explain() - look for COLLSCAN (bad)
db.users.find({ email: "..." }).explain()
Over-indexing:
// Each index adds write overhead// Only index fields used in queries// Monitor index usage: db.users.aggregate([{ $indexStats: {} }])
